The Open Source AI Stack

Open-weight AI models are increasingly viable alternatives to closed systems for software development, offering developers greater ownership, flexibility, and potential cost savings without requiring model-training expertise or local GPU infrastructure. The proposed “MIGHT” stack separates the model, inference provider, gateway or router, harness, and tools such as skills and Model Context Protocol servers, allowing each component to be selected or replaced independently. Large models are presented as better suited to complex, ambiguous, multi-step tasks such as architecture changes and reviews, while smaller models can deliver faster, cheaper performance on narrowly defined work; model selection should therefore depend on the task rather than a single benchmark ranking. Cloud providers, gateways, and local routers enable access to diverse models and simplify switching among providers, while harnesses manage conversations, codebase context, tool calls, permissions, and file changes. Effective use also depends on managing context through fresh sessions and potentially dividing work among planning, implementation, and review models. This composable approach lets developers retain a stable workflow while continuously testing and adopting models, providers, and tools that best fit their requirements.

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